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Alternatives based on transformations of the model have been suggested ([15, 50]) but, as reported in [33], they fall short when compared to SMC methods.
Casdagli et al. [35] give a thorough treatment of such techniques based on transformations of univariate maps, showing how optimal noise reduction can be achieved.
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Here we consider a new approach to solving a one-stage optimization problem which is based on transformation of chance constraints into deterministic ones.
This approach is based on transformation of chance constraints into deterministic ones.
The first is the Short Time series Expression Miner (STEM) algorithm and software developed by Ernst et al., where all genes are clustered into one of a set of pre-defined patterns based on transformation of gene profiles into "units of change" [ 23].
The transient gene expression assay [ 6, 11, 12, 18] is based on transformation of single leaf epidermal cells, followed by subsequent inoculation of transformed leaves with specific powdery mildew isolates.
The method is based on transformation of collections of output functions into state variables.
The method is based on transformation of FSM structure table and replacement of some logical conditions.
Our analysis was based on transformation of the physical domain (yield) into insurer's loss domain (gross loss net of deductibles and reinsurance).
We develop method of solving the two-stage optimization problem with chance constraints based on transformation of a problem with chance constraints into a problem with deterministic constraints.
The solution procedure is based on transformation of the governing equations from physical domain to computational domain and then the discretization of the spatial derivatives by employing the differential quadrature method (DQM) as an efficient and accurate numerical tool.
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